Venice AI, the privacy-focused artificial intelligence (AI) platform founded by bitcoin advocate Erik Voorhees, closed a $65 million Series A round at a $1 billion post-money valuation.
Key Takeaways:
Dragonfly led Venice AI’s $65 million Series A, valuing the company at $1 billion.Venice hit 3 million users and turned profitable in Q1 2026 before raising capital.Investors got 8.98% equity plus VVV warrants exercisable over eight years.Voorhees spent two years building Venice before taking outside money. The platform now counts more than 3 million active users. It processes 1.3 trillion tokens per month and handles over 1.7 million daily API calls. Venice turned profitable in the first quarter of 2026, a rare outcome in an industry where many AI firms still burn cash.
Venice positions itself against mainstream chatbots that log user prompts and store conversation history. The platform encrypts inputs client-side and does not retain conversations on its servers. Users can choose among more than 200 AI models, including open-source options with fewer content restrictions alongside closed-source models from providers like OpenAI and Anthropic.
Investors Skip the Token, Take Equity InsteadRather than sell treasury tokens to raise capital, Venice chose equity. Series A investors received 8.98% of the company, a vesting grant of 1.5 million VVV, and warrants to purchase up to 5 million additional VVV over eight years. If investors exercise those warrants in full, total capital raised could reach $131.5 million.
Voorhees Points Proceeds at Owned InfrastructureVenice plans to use the funds to build proprietary data centers, reducing its reliance on leased compute and improving margins. The company also intends to expand its customer base, enter new markets, and pursue acquisitions.
The raise arrives amid ongoing debate over AI safety, censorship, and data collection practices across the industry. Venice’s approach, betting on user privacy and minimal data retention, stands apart from competitors that rely on stored user data to train and refine their models.


















